Small airway dysfunction is an early physiological characteristic of idiopathic pulmonary fibrosis : a retrospective cohort study

doi:10.21203/rs.3.rs-2095843/v1

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Small airway dysfunction is an early physiological characteristic of idiopathic pulmonary fibrosis : a retrospective cohort study

https://doi.org/10.21203/rs.3.rs-2095843/v1

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Background: Recently, the central position of small airways in the pathogenesis of idiopathic pulmonary fibrosis(IPF) has been gradually recognized and accepted with evidence of small airway anatomical and genetic abnormalities. However, the small airway physiology of IPF patients remained unclear. This study aimed to assess the small airway physiology of IPF patients using pulmonary function test (PFT).

Methods: We retrospectively analyzed 138 IPF patients' and 186 control patients' small airway lung function data and medical records. A 1:1 propensity score match(PSM) for age, sex, smoking status and BMI was completed priors to the group comparison between IPF patients and controls. In addition, patients were divided into three groups according to FVC decline degree with cut-off values of 75% and 55%, and the small airway function was compared between the three groups. The impulse oscillometry system (IOS) diagnosis value was evaluated in 60 IPF patients.

Results: The total cohort comprised 138 subjects, with 53(38.4%) diagnosed with SAD. After PSM, IPF patients were predisposed to SAD compared with controls (38.0% vs.15.2%, p=0.001). Compared small airway function between patients with different disease severities, we found even for patients in the mild disease group whose FVC is almost normal, 30.6% (n=22) were diagnosed with spirometry-SAD. And we found MEF50(98.2% vs. 87.4% vs. 52.4%, p=0.001), MEF25(74.2% vs.71.9% vs. 61.3%, p=0.062) and MMEF(77.2% vs. 74.6% vs. 44.6%, p=0.002) were deteriorated with the severity of disease. The incidence of spirometry-SAD in more severe patients was higher than those with less severe disease(p=0.021). Applying IOS to 60 IPF patients, 41(68.3%) were diagnosed with IOS-SAD. There was poor concordance between spirometry and IOS for the diagnosis of SAD (Kappa value=-0.068, p=0.542). IOS showed higher sensitivity than spirometry. For patients with abnormal lung function (FVC<80%),this advantage could be even more obvious.

Conclusions: This study revealed that SAD was a common lesion of IPF patients. It should be emphasized that SAD could occur at the early stages of disease when the lung capacity has not decreased, and the incidence and degree of dysfunction were progressively aggravated with disease progression. Additionally, IOS was a powerful complementary tool for diagnosing SAD, especially for IPF patients with decreased lung function.

Idiopathic pulmonary fibrosis (IPF)

Small airway dysfunction (SAD)

Pulmonary function test (PFT)

Spirometry

Impulse oscillometry system (IOS)

Idiopathic pulmonary fibrosis (IPF) is a chronic, fibrotic interstitial lung disease of unknown etiology with increasing incidence[1–3]. IPF patients typically experience a progressive decline in lung function, which finally lead to fatal respiratory failure[4, 5], with a median survival of 2–3 years following initial diagnosis[2]. Proverbially, IPF presents a pattern of usual interstitial pneumonia (UIP). Histopathologically, it shows fibrotic remodeling leading to abnormal activation of alveolar epithelial cells (AECs) and honeycomb [6–8]. Radiologically, it shows bilateral reticulation and honeycombing, predominantly distributed in peripheral and lower lobes with traction bronchiectasis[9]. Currently, most researchers define IPF as a disease occurring in the alveolar region of the lung, and airways were neglected in IPF. Based on the pathogenesis of IPF, nintedanib and pirfenidone are developed for

antifibrosis. However, they have been demonstrated only slow but not halt disease progression[10, 11]. Therefore, effective therapeutic targets are urgently needed, as well as treatments that can control disease progression, reduce symptoms, and prolong life expectance.

In recent years, as the knowledge of diseases develops, a growing body of evidence has been provided that small airway abnormality plays a potential role in the pathogenesis of IPF. Forty years ago, FULMER first proposed that idiopathic pulmonary fibrosis affects small airways and the alveoli[12]. Years later, Stijn E Verleden and his colleagues proved that loss of terminal bronchioles occurs within regions of minimal fibrosis[13]. Ikezoe and colleagues published a further exploration demonstrating that terminal bronchioles undergo traction bronchiolectasis, resulting in the formation of honeycomb cysts[14]. Moreover, several pieces of research found peripheral mucociliary genetic dysfunction. The evidence above indicated the importance of small airways in pathogenesis of IPF, and the small airway is a potential effective therapeutic target.

However, even though it is agreed that IPF is a disease involving small airways, most researches focused on the structural changes of small airways. A few research have assessed the small airway physiology of IPF patients. In practice, the value of pulmonary function tests (PFT) on the physiological assessment of small airways is widely recognized. Therefore, we conducted a retrospective study to analyze the small airway physiology of IPF patients with spirometry and impulse oscillometry system (IOS). In addition, we explained the different values of the two tools on the diagnosis of SAD. We also explored the clinical features and risk factors for SAD in IPF patients.

Patients

We retrospectively enrolled IPF patients referred to Shanghai Pulmonary Hospital and Shanghai Jiao Tong University Affiliated Sixth People’s Hospital between 2012 and 2018. The diagnosis of IPF was based on the criteria provided by the American Thoracic Society (ATS)/European Respiratory Society (ERS)/Japanese Respiratory Society (JRS)/Latin American Thoracic Society (ALAT). The main inclusion criteria for the study were as follows:1) confirmed diagnosis of IPF by three pulmonologists; 2) underwent spirometry and plethysmography; 3) with reliable and complete data. IPF patients were excluded when:1) without recorded high-resolution computed tomography (HRCT) of the chest; 2) with other known causes of interstitial lung disease, such as connective tissue disease with autoimmune features, domestic or occupational environmental exposure, and drug toxicity; 4) without complete spirometric and plethysmographic parameters data; 5) without valid demographic data. Controls were patients undergoing surgery for benign lung nodule resection, and patients with prior history of lung resection surgery or without complete pulmonary function data or demographic data were excluded. Data of baseline demographic variables were collected, including age, gender, smoking status, body mass index (BMI), complication, and lung function parameters (spirometry, plethysmography, and IOS). A smoker was defined as a person who smoked at least 100 cigarettes in his or her lifetime. All information on these patients was collected from the electronic medical records system. This study was approved by the Institutional Review Board of Shanghai Pulmonary Hospital and Shanghai Jiao Tong University Affiliated Sixth People’s Hospital.

Spirometry and Plethysmography

A computerized spirometer (MasterScreen, Jaeger, Germany) was used for the pulmonary function test under the recommendations issued by the ATS/ERS. Lung function parameters included forced vital capacity (FVC), forced expiratory volume in 1 second/FVC (FEV1/FVC), residual volume (RV), total lung capacity (TLC), RV/TLC, Rtot (total airway resistance), Reff (effective airway resistance), the diffusing capacity for carbon monoxide (Dlco), maximum expiratory flow at 50% (MEF50%), maximum expiratory flow at 25% (MEF25%), and maximal mid-expiratory flow (MMEF). All data were expressed as a percentage of predicted values.

Impulse oscillometry (IOS)

The IOS (MasterScreen IOS, Jaeger, Germany) was performed according to the criterion recommended by ESR. Subjects should take a sitting position with normal respiration, raise their head slightly and straighten their neck. Nasal clips and manual compression of the cheeks were used to avoid cheek vibration and escape of air via the nostrils. After the instrument showed that their breathing was stable and regular, the records were started. The duration was 30-45 seconds with one-minute intervals, and the examination was repeated 3-4 times for each subject. Ultimately, the best values were recorded for further analysis. We evaluated the following IOS parameters: resistance at 5 Hz (R5), resistance at 20 Hz (R20), the difference between resistance at 5 Hz and 20 Hz (R5–R20), and reactance at 5 Hz (X5), resonant frequency (Fres). Data are expressed as absolute numbers.

The diagnostic criteria of SAD

The diagnosis of small airway dysfunction (SAD) was based on lung function, including spirometry and IOS. Three indicators of spirometry were used to assess spirometry-SAD, namely MEF25, MEF50, and MMEF. Spirometry-SAD was diagnosed when at least two of these three indicators were less than 65% of predicted values. According to the previous study, IOS-defined SAD (IOS-SAD) was defined by R5–R20 greater than 0.07kPa/(L/S)[15]. X5, Fres were used to diagnose SAD with a cut-off values of -0.12kPa/(L/S) and 14.14 Hz.

GAP model

The GAP score was calculated using the method described by Brett Ley[16]. Clinical variables used for the calculation included gender (Female: 0 points, male: 1 point), age (0–2 points), FVC (%)(0–2 points), and Dlco (%)(0–3 points), the specific criteria were listed in Table2. Based on GAP score, patients were divided into three groups: GAPⅠ (GAP score 0-3), GAPⅡ (GAP score 4-5), and GAPⅢ (GAP score 6-8).

Statistical analysis

The distribution of variables was assessed using the Kolmogorov–Smirnov goodness-of-fit test. Continuous variables were expressed as mean ± SD for normally distributed variables and median [ interquartile range, Inter-Quartile Range(IQR) ] for non-normally distributed variables. In contrast, categorical variables were described as percentages. Independent T-test, Mann–Whitney U test, Kruskal-Wallis test, and Pearson’s Chi-square test were used for comparisons of different situations. Pearson correlation coefficient was used to obtain relationships between variables. Odds ratios (OR) and 95% confidence intervals (95%CI) were calculated using binary logistic regression to analyze associations between potential risk factors and SAD. The concordance was examined using the Kappa concordance test and McNem’r's test. All tests were two-sided, and the demarcation of statistical significance was p value < 0.05. Statistical analyses were performed with the SPSS 26.0.

In particular, in order to reduce the bias in scientifically evaluating the difference in lung function between IPF patients and control subjects, the propensity score matching (PSM) analysis method was performed. PSM was used to eliminate selection bias due to uneven distribution between the two groups and to compare them in a 1:1 ratio in this non-randomized study. Propensity scores for all patients were calculated using multiple logistic regression with the following covariates: age, gender, smoking status, and BMI.

Characteristics of study subjects

A total of 138 eligible patients with IPF were enrolled in this study (Figure1). The median age of the IPF patient population was 63.0 years (IQR:59.0, 69.0). 73.2%(n=101) enrolled patients were male. Most of the patients (n=84, 60.9%) were smokers. Regarding comorbidities, there were 33(23.9%) patients complicated with emphysema, 42(30.4%) patients with hypertension, 24(17.4%) with diabetes, and 4(2.9%) with lung cancer. Among the total 138 IPF patients, 53 patients (38.4%) were diagnosed with spirometry-SAD.

Comparison of small airway function between IPF patients and controls

Before PSM, compared with the control group composed of 186 participants without IPF, IPF patients were more likely to be smokers(p<0.001). In addition, the differences in age (63.0y vs. 59.0y, p<0.001), sex (male:73.2% vs. 36.0%, p<0.001) and BMI (24.4kg/m² vs. 23.6 kg/m², p=0.032) were significant. After a 1:1 PSM analysis, 79 matched pairs were obtained, and all clinical characteristics were balanced between the two groups. The pulmonary function parameters differed significantly between IPF patients and controls after PSM practice. In the PSM groups, there were significant differences in FVC (75.9% vs. 85.3%, p=0.001) and DLco (69.2% vs. 94.1%, p<0.001) between the two groups (Table 1). IPF patients also showed lower FEV1/FVC, RV, and TLC, RV/TLC. In addition, compared with controls, IPF patients tended to manifest poorer functional status on indicators which reflect small airway function: MEF25(68.3% vs. 90.4%, p= 0.001), MMEF (69.5% vs. 86.8%, p<0.001). According to the criteria of SAD mentioned earlier, 30(38.0%) IPF patients were diagnosed with spirometry-SAD among 79 match pairs, while only 12(15.2%) controls were diagnosed with spirometry-SAD. The differences were statistically significant (p=0.001).

Table 1 Clinical characteristics and pulmonary function of patients with and without IPF

	Before PSM			After PSM
	IPF patients (n=138)	Control (n=186)	p	IPF patients (n=79)	Control (n=79)	p
Age, y	63.0(59.0,69.0)	59.0(50.0,66.3)	<0.001	62.4±9.0	62.1±9.0	0.806
Gender: male, n (%)	101(73.2)	67(36.0)	<0.001	44(55.7)	36(45.6)	0.203
Smoker, n (%)	84(60.9)	28(15.1)	<0.001	26(32.9)	26(32.9)	1.000
BMI, kg/m²	24.4±3.1	23.6±3.2	0.032	24.0±3.4	24.8±3.2	0.136
*Pulmonary function test*
FVC (% predicted)	76.7(59.2,87.5)	83.8(77.7,91.2)	<0.001	75.9±19.9	85.3±12.2	0.001
FEV1/FVC (% predicted)	104.2(97.2,110.4)	92.2(85.8,102.0)	<0.001	103.3(97.0,110.0)	91.5(86.0,110.8)	0.005
RV (% predicted)	88.8(78.3,110.5)	116.4(103.2,131.8)	<0.001	94.5(78.5,116.1)	114.7(99.7,132.2)	<0.001
TLC (% predicted)	79.6(68.3,95.6)	93.9(85.1,103.1)	<0.001	83.2±18.6	92.1±10.9	<0.001
RV/TLC (% predicted)	113.5(103.0,126.2)	122.0(113.6,132.7)	<0.001	115.7(103.0,128.0)	121.7(109.3,133.8)	0.046
DLco (% predicted)	64.4(47.4,78.9)	100.1(85.1,111.9)	<0.001	69.2±24.6	94.1±19.3	<0.001
MEF50 (% predicted)	87.9(64.6,116.0)	85.2(68.7,101.2)	0.268	92.8±34.7	90.7±26.4	0.675
MEF25 (% predicted)	70.4(45.8,101.6)	80.2(61.4,108.6)	0.011	68.3(43.1,95.5)	90.4(70.3,117.0)	<0.001
MMEF (% predicted)	70.8(51.8,99.1)	83.3(66.8,99.9)	0.003	69.5(52.8,99.0)	86.8(70.7,109.6)	0.001
MEF50(%)＜65%(n,%)	34(24.6)	37(19.9)	0.307	18(22.8)	12(15.2)	0.224
MEF25(%)＜65%(n,%)	60(43.5)	53(28.5)	0.005	34(43.0)	17(21.5)	0.004
MMEF(%)＜65%(n,%)	57(41.3)	39(21.0)	<0.001	33(41.8)	13(16.5)	<0.001
Spirometry-SAD, n (%)	53(38.4)	39(21.0)	0.001	30(38.0)	12(15.2)	0.001

The data are presented as mean ± SD, median (interquartile range)

Abbreviations: IPF=Idiopathic pulmonary fibrosis, PSM=propensity score match, BMI=Body-mass index, FVC=forced vital capacity, FEV1=forced expiratory volume in 1 second, RV=residual volume, TLC total lung capacity, DLco=the diffusing capacity for carbon monoxide, MEF=maximum expiratory flow, MMEF=maximal mid-expiratory flow, SAD=small airway dysfunction.

Small airway function of patients of varying disease severity

To explore the small airway status of IPF patients with varying levels of disease severity, we divided them into three groups according to FVC decline degree: mild (FVC%>75%), moderate (55%≤FVC%≤75%), and severe(FVC%<55%). (Table2) With increasing severity of FVC, values of FEV1/FVC (p=0.004), RV (p<0.001), TLC (p<0.001), and RV/TLC (p<0.001) significantly decreased, showing progressive restrictive pulmonary ventilation disorder. The value of Rtot (p=0.005) and Reff (p=0.018) were higher in the more severe group. Lung diffusing capacity was also altered in three groups(p<0.001). More than half of patients with severe IPF had spirometry-SAD (n=16, 61.5%). Even in the mild disease group, 30.6% (n=22) of patients were diagnosed with spirometry-SAD. Furthermore, the more severe the FVC decline was, the worse the small airway function was. The incidences of SAD between mild, moderate, and severe groups were significantly different(30.6% vs. 37.5% vs. 61.5, p=0.021). Pairwise contrasts of the three group is showed in Figure 2.

Table 2 Pulmonary function data of different disease severity level

	Mild FVC%＞75% (n=72)	Moderate 55%≤FVC%≤75% (n=40)	Severe FVC%＜55% (n=26)	p
Age, y	63.0(59.0,69.0)	63.5(58.0,71.5)	62.0(57.0,66.0)	0.732
Gender: male, n (%)	59(81.9)	27(67.5)	15(57.7)	0.036
Smoker, n (%)	48(66.7)	21(52.5)	15(57.7)	0.316
BMI, kg/m²	24.0±3.0	24.8±3.0	24.8±3.6	0.254
*Pulmonary function test*
FVC (% predicted)	87.3(82.4,100.3)	63.6(59.7,68.7)	51.2(45.3,54.0)	<0.001
FEV1/FVC (% predicted)	100.6±9.7	106.4±9.3	106.0±9.2	0.004
RV (% predicted)	103.6(84.2,117.1)	81.5(67.8,99.2)	78.5(66.5,104.7)	<0.001
TLC (% predicted)	94.8(83.8,103.3)	70.6(63.8,77.4)	60.9(55.1,71.4)	<0.001
RV/TLC (% predicted)	109.2(100.0,116.0)	114.4(103.6,127.2)	126.0(115.9,146.9)	<0.001
Rtot	112.7(95.9,143.0)	139.0(109.1,175.8)	148.6(106.5,172.8)	0.005
Reff	101.2(84.7,127.9)	120.4(86.6,155.4)	134.1(97.8,167.1)	0.018
DLco (% predicted)	78.2±22.1	56.7±14.8	43.5±11.1	<0.001
MEF50 (% predicted)	98.2(69.8,123.1)	87.4(71.3,115.9)	52.4(48.6,83.0)	0.001
MEF25 (% predicted)	74.2(48.9,107.1)	71.9(42.5,102.4)	61.3(34.6,71.2)	0.062
MMEF (% predicted)	77.2(54.5,104.7)	74.6(54.6,99.4)	44.6(38.9,73.7)	0.002
MEF50(%)＜65%(n,%)	12(16.7)	8(20.0)	14(53.8)	0.001
MEF25(%)＜65%(n,%)	28(38.9)	16(40.0)	16(61.5)	0.119
MMEF(%)＜65%(n,%)	23(31.9)	16(40.0)	18(69.2)	0.004
Spirometry-SAD, n (%)	22(30.6)	15(37.5)	16(61.5)	0.021

The data are presented as mean ± SD, median (interquartile range)

Abbreviations: BMI = Body-mass index; FVC = forced vital capacity, FEV1 = forced expiratory volume in 1 second, RV = residual volume, TLC = total lung capacity, Rtot = total airway resistance, Reff = effective airway resistance, DLco = the diffusing capacity for carbon monoxide, MEF = maximum expiratory flow, MMEF = maximal mid-expiratory flow, SAD = small airway dysfunction.

The diagnostic value of IOS in identifing SAD

Although spirometry was widely used to define SAD, IOS may also be an effort-independent method that is convenient and more sensitive to detecting SAD in IPF patients. In IPF patients, the values of MEF50 and MMEF were significantly correlated with many IOS parameters (Figure 3), although the correlation was very limited.

In our study, there were 60 patients available for the advanced analysis of IOS. IOS-SAD ratios were 68.3%, which was higher than those of SAD diagnosed by spirometry (n=25, 41.7%). The Cohens kappa agreement test was used to check the agreement and concordance between IOS and spirometry in the diagnosis of SAD. As described in Table 3, the concurrence of spirometry and IOS was generally low in the assessment of SAD (Kappa=-0.068, p=0.542). The results of McNemar's test remained consistent (p=0.009). Taking spirometry as standard, the sensitivity and specificity of IOS were 64.0% and 28.9%, respectively. The area under the curve (AUC) was 0.463 (0.313-0.612). For 35 patients diagnosed with non-SAD by spirometry, 25 patients were diagnosed with SAD by IOS. For patients with normal FVC (FVC%>80%), the number of IOS-only SAD was twice that of spirometry. Moreover, for patients with decreased FVC(FVC%<80%), IOS-only SAD were three to four time more than spirometry-only SAD. IOS criteria exhibited enhanced sensitivity to spirometry, especially in those patients with FVC less than 80% predicted(Table 3). The diagnosis values of other IOS parameters except for R5-R20 were listed in supplementary statement table 1(Additional file 1).

Table 3 The consistency test between spirometry and IOS parameters. (N=60)

	Impulse Oscillometry	Spirometry		Kappa test		McNemar’s test
		Negative (-)	Positive (+)	Kappa value	p	p
Patients with normal FVC (n=11)	Negative (-)	6	4	-0.125	0.537	0.388
	Positive (+)	8	3
Patients with decreased FVC (n=39)	Negative (-)	4	5	-0.083	0.519	0.017
	Positive (+)	17	13
Total Patients	Negative (-)	10	9	-0.068	0.542	0.009
(n=60)	Positive (+)	25	16

Abbreviations: FVC = forced vital capacity

IOS parameters related to peripheral airways resistance, including R5-R20[0.08kPa/(L/S) vs. 0.11kPa/(L/S), p=0.019], X5[-0.11kPa/(L/S) vs. -0.17 kPa/(L/S), p<0.001], Fres (15.72Hz vs. 17.59 Hz, p=0.001), were all different between IPF patients and controls. According to diagnostic criteria of SAD based on IOS(IOS-SAD), 41(68.3%) of 60 IPF patients were with R5-R20 greater than 0.07 kPa/(L/S). Compared with controls, IPF patients were more susceptible to IOS-SAD (52.2% vs. 68.3%, p=0.090) (Table 4).

Table 4 Differences in IOS parameters between IPF patients and controls.

IOS parameter	Control (n=46)	IPF patients (n=60)	p
R5, [kPa/(L/S)]	0.36(0.27,0.44)	0.35(0.26,0.44)	0.779
R20, [kPa/(L/S)]	0.28(0.22,0.34)	0.23(0.19,0.31)	0.059
R5-R20, [kPa/(L/S)]	0.08(0.04,0.12)	0.11(0.05,0.15)	0.019
X5, [kPa/(L/S)]	-0.11(-0.14,-0.08)	-0.17(-0.25,-0.12)	<0.001
Fres, (Hz)	15.72(12.00,18.5)	17.59(15.4,21.5)	0.001
R5–R20>0.07kPa/(L/S)	24(52.2)	41(68.3)	0.090
X5<−0.12 kPa/(L/S)	19(41.3)	43(71.7)	0.002
Fres> 14.14Hz	29(63.0)	56(93.3)	<0.001

The data are presented as median (interquartile range)

Abbreviations: IPF=Idiopathic pulmonary fibrosis, IOS=impulse oscillometry, R5=resistances at 5 Hz, R20=resistances at 20 Hz, R5-R20=resistances at 5 and 20 Hz, X5=reactance at 5 Hz, Fres=resonant frequency in Hz.

The IOS parameters also altered in three FVC groups(Figure 4). The value of R5-R20, X5, Fres were different between mild and moderate groups(R5-R20:p=0.002, X5:p=0.065, Fres:p=0.001).

Clinical features and PFT parameters of IPF patients with or without SAD

Compared with patients without spirometry-SAD, patients with spirometry-SAD were more likely to be smokers (58.8% vs. 64.2%, p=0.533). More patients with SAD were in GAP stage Ⅱ-Ⅲ than patients without SAD (23.5% vs. 39.6%, p=0.044). There were significant differences in FVC (80.0% vs. 68.3%, p=0.034) and RV/TLC (110.5% vs.116.0%, p=0.011) as well as Rtot (118.2% vs. 145.1%, p=0.003) and Reff (102.1% vs. 131.8%, p=0.001) between IPF patients with SAD and without SAD.(Table 5)

Table 5 Clinical characteristic and pulmonary function differences between patients with SAD and without SAD.

	Non-SAD (n=85)	SAD (n=53)	p
Age, y	64.6±8.6	62.6±8.7	0.140
Gender：male, n (%)	65(76.5)	36(67.9)	0.270
Smoker, n (%)	50(58.8)	34(64.2)	0.533
BMI, kg/m²	24.2±3.1	24.8±3.1	0.471
Emphysema, n (%)	19(22.4)	14(26.4)	0.586
GAP score			0.044
GAP Ⅰ, n (%)	65(76.5)	32(60.4)
GAP Ⅱ-Ⅲ, n (%)	20(23.5)	21(39.6)
*Pulmonary function test*
FVC (% predicted)	80.0(64.0,90.3)	68.3(55.1,87.3)	0.034
FEV1/FVC (% predicted)	107.7±7.7	96.3±8.8	<0.001
RV (% predicted)	88.5(78.5,110.0)	100.4(81.2,113.9)	0.281
TLC (% predicted)	82.6±18.8	79.8±18.3	0.397
RV/TLC (% predicted)	110.5(100.2,120.4)	116.0(106.8,127.6)	0.011
Rtot (% predicted)	118.2(91.6,147.2)	145.1(105.3,172.8)	0.003
Reff (% predicted)	102.1(78.3,131.2)	131.8(95.8,165.5)	0.001
DLco (% predicted)	65.9(52.2,80.0)	62.5(44.6,75.8)	0.219

The data are presented as mean ± SD, median (interquartile range)

Abbreviations: SAD=small airway dysfunction, BMI=Body-mass index; FVC = forced vital capacity, FEV1 = forced expiratory volume in 1 second, RV = residual volume, TLC = total lung capacity, Rtot = total airway resistance, Reff = effective airway resistance, DLco = the diffusing capacity for carbon monoxide.

Risk factor analysis for IPF patients with SAD

Table 6 summarizes the ORs of SAD. Multiple logistics regression revealed that smoker was an independent risk factor for SAD (p=0.049). The risk of SAD was approximately threefold higher in those smokers than in non-smokers.

Table 6 Risk factors for SAD in IPF patients. (N=138)

	Univariate			Multivariate
	OR	95% CI	p	OR	95% CI	p
Age, y	0.973	0.934-1.013	0.187	0.981	0.939-1.024	0.374
Male, n (%)	0.652	0.303-1.399	0.272	0.296	0.083-1.061	0.062
Smoker, n (%)	1.253	0.617-2.544	0.533	3.354	1.005-11.193	0.049
BMI, kg/m²	1.061	0.949-1.186	0.302	1.049	0.931-1.182	0.433
FVC (% predicted)	0.980	0.963-0.998	0.026	0.978	0.956-1.001	0.056
DLco (% predicted)	0.993	0.979-1.007	0.343	1.008	0.989-1.028	0.413

Abbreviations: OR = odds ratio, CI = confidence interval, FVC = forced vital capacity, DLco = the diffusing capacity for carbon monoxide

Small airway function in different GAP groups

To explore the relationship between SAD and prognosis, we divided study subjects into two groups according to the GAP stage, namely GAP Ⅰ and GAP Ⅱ-Ⅲ (Table 7). Supplementary statement table 2(Additional file 1) showed GAP score distribution. In our study, 97 patients were in GAP stage Ⅰ, and 41 were in GAP stage Ⅱ-Ⅲ. With increasing stage of GAP, values of FVC (84.5% vs. 55.1%, p<0.001), FEV1/FVC (102.1% vs. 106.2%, p=0.028), RV (96.4% vs. 78.5%, p<0.001), TLC (88.0% vs. 66.0%, p<0.001), DLco (73.1% vs. 41.3%, p<0.001) significantly decreased, while RV/TLC (111.3% vs. 120.4%, p=0.019) increased. Apart from that, patients in GAP stage Ⅱ-Ⅲ showed lower MMEF50 (91.0% vs. 76.8%, p=0.040), MEF25 (72.2% vs. 62.1%,p=0.148) and MMEF (71.1% vs. 62.1%, p=0.070), indicating descending small airway function. Among patients in GAP stage Ⅱ-Ⅲ, more than half patients had spirometry-SAD (n=21, 51.2%). Compared with GAP Ⅰ patients, more GAP Ⅱ-Ⅲ patients were diagnosed with spiratory-SAD (33.0% vs. 51.2%), and the difference in SAD proportion between the two groups reached statistical significance(p=0.044). As showed in Supplementary statement table 3(Additional file 3), patients in GAP stage Ⅱ-Ⅲ showed worse R5-R20, X5 and Fres compared with controls and patients in GAP stage Ⅰ (p=0.008; p<0.001; p=0.001).

Table 7 Differences in spirometry parameters between GAP subgroups. (N=138)

Variable	GAP points	GAP Stage I (n=97)	GAP Stage II-III (n=41)	p
Gender, n (%)				0.676
Female	0	27(27.8)	10(24.4)
Male	1	70(72.2)	31(75.6)
Age(y), n (%)				<0.001
≤60	0	44(45.4)	6(14.6)
61-65	1	23(23.7)	8(19.5)
>65	2	30(30.9)	27(65.9)
Physiology
FVC (% predicted)				<0.001
≥75	0	70(72.2)	2(4.9)
50-75	1	26(26.8)	25(61.0)
<50	2	1(1.0)	14(34.1)
DLco (% predicted)				<0.001
>55	0	81(83.5)	8(19.5)
36-55	1	16(16.5)	24(58.5)
≤50	2	0(0.0)	9(22.0)
*Pulmonary function test*
FVC (% predicted)		84.5(68.1,96.8)	55.1(47.4,67.1)	<0.001
FEV1/FVC (% predicted)		102.1±9.1	106.2±11.0	0.028
RV (% predicted)		96.4(81.5,115.4)	78.5(64.5,91.3)	<0.001
TLC (% predicted)		88.0±16.2	66.0±14.4	<0.001
RV/TLC (% predicted)		111.3(102.4,122.8)	120.4(105.6,133.0)	0.019
DLco (% predicted)		73.1(58.3,86.1)	41.3(35.4,51.1)	<0.001
MEF50(% predicted)		91.0(69.9,116.3)	76.8(47.2,119.1)	0.040
MEF25(% predicted)		72.2(49.5,102.9)	62.1(37.2,91.5)	0.148
MMEF (%predicted)		71.1(54.9,98.2)	62.1(39.4,99.4)	0.070
MEF50(%)＜65%(n,%)		16(16.5)	18(43.9)	0.001
MEF25(%)＜65%(n,%)		39(40.2)	21(51.2)	0.233
MMEF(%)＜65%(n,%)		36(37.1)	21(51.2)	0.124
Spirometry-SAD, n (%)		32(33.0)	21(51.2)	0.044

The data are presented as mean ± SD, median (interquartile range)

Abbreviations: FVC=forced vital capacity, FEV1=forced expiratory volume in 1 second, RV=residual volume, TLC=total lung capacity, DLco=the diffusing capacity for carbon monoxide, MEF =maximum expiratory flow, MMEF =maximal mid-expiratory flow, SAD=small airway dysfunction.

Our study evaluated the small airway physiology measured by pulmonary function tests in the cohort of IPF. We firstly demonstrated that IPF patients were susceptible to SAD compared to propensity score matched controls. In addition, we highlighted that SAD could happen at an early stage of the disease when the decline of FVC was mild, and the incidence of SAD was associated with disease progression. Further, we explored value of IOS in assessing small airway physiology in IPF patients. In our study, IOS shows higher sensitivity in diagnosing SAD, especially for patients with abnormal lung function (FVC%<80%), indicating its applicability in IPF patients.

In the past, the small airways less than 2 mm in diameter were coined as "quiet zone" within the lungs. This was because these structures were felt to contribute little to lung mechanics and remain clinically silent and undetectable[17-19]. Similarly, the importance of small airways in IPF was ignored for a long time. With the advancement of detection technologies, micro-computed tomography(micro-CT) was proved to be an effective method of detecting small airway pathology. Using a combination of micro-CT, multidetector CT, and histology, Verleden found that the number of terminal bronchioles in IPF tissue was reduced compared with healthy (control) lungs [13]. Most noteworthy was that even in areas of minimal fibrosis, there was a 57% reduction in the number of terminal bronchioles, suggesting the role of small airways in early disease. Recently, Ikezoe and colleagues published a further exploration of the relationship between small airway loss and fibrosis in IPF[14]. In agreement with previous studies, Ikezoe found a reduction in the number of conducting terminal and respiratory transitional bronchioles in patients with IPF. Further, it has been proved that small airways participate in lung tissue remodeling. Those findings suggested that airway-specific changes may even precede the fibroproliferative changes in IPF. Based on airway-specific anatomical abnormalities and genetic risks of the peripheral airways in IPF, an airway-centric model of IPF emerged[20]. According to this model, when the distal airway epithelium encounters minor injuries, epithelial progenitor cells would be activated, mucus production would increase, and aberrant cell differentiation would restore homeostasis. For individuals with heightened genetic risk, this injury-repair cycle will be potentiated, and those individuals will progress to a state of chronic injury and repair. This chronic cycle will cause a permanent change of progenitor populations, impair mucociliary clearance, and concurrently result in loss of distal airways, and honeycomb cysts will eventually appear. This model highlights that before hallmarks of IPF begin to appear, chronic injury and repair of distal airway epithelium are already initiated. Therefore, early in the IPF disease, impairment of mucociliary clearance of distal airway epithelium could cause small airway obstruction, in combination with the absence of distal airways, which would result in airflow limitation in small airways. The changes preceded the alterations of lung volumes and capacities. Now that small airway abnormalities had been proved in IPF patients from histopathological perspectives, our study demonstrated that IPF patients presented SAD from the physiological perspective. And as described in our study, before impairment of alveolar structure and function leading to decreased diffusion function, IPF patients have been showed SAD. That indicated small airway damage is an early event in IPF.

Indeed, how to evaluate small airways and what is the "gold standard" for making the definitive diagnosis of SAD in IPF patients in clinical practice are important issues[21]. This is partly due to the relative inaccessibility to biopsy and the small size, making their imaging difficult. At present, spirometry is the most widely used test both in the diagnosis and stratification of the severity of lung disease. Spirometry parameters reflecting small airway function include FEV1, FEV 3 /FVC, MMEF, MEF50%, MEF25%[22, 23]. Based on the Guideline for pulmonary function testing in primary care (2018) published by the Chinese Medical Association (CMA) and previous studies, SAD was diagnosed when at least two of these three indicators (MMEF, MEF50%, MEF25%) were less than 65% of predicted values in this study[23-28]. By spirometry, we identified that about 40% of IPF patients presented SAD significantly more frequently than the control group (21.0%). The incidence of SAD in IPF patients was comparable to that of small airway diseases like emphysema and asthma[29, 30]. Histopathological, it has been proved that terminal bronchioles reduced starts early in IPF. According to previous studies, FVC correlated with the extent of interstitial disease severity [31]. Therefore, we divided patients into three groups of different degrees of disease severity according to the value of FVC physiologically. In the mild group, the alternation of FVC has not yet occurred, and 30.6% of patients were also observed with SAD. The incidence is much higher than controls. Compatible with the histopathological changes, small airway physiologic abnormalities were found to occur at the early stage of IPF disease. Considering that small airway abnormalities physiologically are only reported if a certain amount of small airways accumulate beyond the threshold, the possibility cannot be ruled out that structural changes of the small airway may already occur early in patients without SAD.

Another strength of our study was that we used IOS to assess the small airway physiology of IPF. IOS is a simple, noninvasive method using the forced oscillation technique that can more sensitively detect SAD than spirometry because it is independent of the effort and only requires performing during normal tidal breathing[32]. Therefore, it has been used for patients with airway diseases, such as chronic obstructive pulmonary disease (COPD), asthma, as well as ILD[15, 33]. Many researches found discordance between spirometry and IOS[34]. That is because MEF is describe by volume change per unit of time to indicate airway resistance, while IOS is not subject to the constraint of volume. Therefore, IOS show increased sensitivity than spirometry. Regrettably, different from the wide application of IOS in diagnosing SAD in asthma and COPD patients, few studies have explored SAD in IPF patients. In a study by KazutakaMori, X5 seems to be the most helpful parameter. Expiratory X5 values were less negative than inspiratory values in patients with IP[35]. Another study found increased R5 and decreased X5 compared with controls[36]. In our study, we preliminarily analyzed IOS parameters of 60 IPF patients and found that 68.3% of patients were diagnosed with IOS-SAD based on the R5-R20 value, which exhibited enhanced sensitivity compared with spirometry. The finding affirmed the value of IOS in diagnosing SAD in IPF. However, in line with findings from previous studies [34], there is discordance between spirometry and IOS in diagnosing SAD. The reasons for this may be MMEF of spirometry was directly dependent on FVC, and changes in FVC will affect the flow-volume curve[21]. In our study, for the patients with abnormal lung function (FVC%<80%), the sensitivity of IOS parameters was much more significant than spirometry. IOS manifested more vital diagnostic value as a complementary method. It is necessary to assess small airway physiology with the complementary method of IOS and spirometry. A study with a larger sample size of IOS data is required to confirm the results.

The GAP model is a clinical prediction model developed by Ley and his colleagues in 2012[16]. The model uses 4 variables: gender (G), age (A), FVC% predicted, and DLco% predicted, commonly measured in clinical practice. Admittedly, the GAP model showed robust predictive power in patients with IPF. In their study, 1-year mortality of GAP stage I, II, and III were 6%, 16%, and 39%, respectively. In another Chinese study, the median survival time of GAP stage I, II, and III was 64.78, 35.33, and 31.60 years[37]. A higher GAP stage was correlated with worse survival in IPF patients. Our study found that there was a higher incidence of SAD in patients at GAP stage II-III who were supposed to have worse prognoses. Furthermore, with increasing stage of GAP, the small airway function worsens. However, our study inferred that SAD might affect patients' prognosis based on the relation between GAP stage and survival time found in other studies. Additional studies with survival analysis of IPF are needed to validate the current results further.

Early detection of SAD in IPF patients is the first step to achieving the goals of precision medicine targeting small airways. Several genes were reported to be overexpressed in IPF airway epithelial cells, strongly suggesting a role of bronchial and bronchiolar epithelial cells in IPF etiology[38]. These genes included mucin-5B (MUC5B), toll interactive protein (TOLLIP), desmoplakin (DSP), family with sequence similarity 13 member A (FAM13A), and A-kinase anchor protein 13 (AKAP13). MUC5B is being extensively studied in IPF. Max A. Seibold found MUC5B may be involved in the pathogenesis of pulmonary fibrosis with MUC5B expressed in bronchiolo-alveolar epithelia of IPF and the characteristic IPF honeycomb cysts[39]. Another study indicated that the MUC5B promoter variant rs35705950 enhances the expression of MUC5B in the bronchiole-alveolar epithelia in IPF lungs[40]. Based on these findings, MUC5B was a validated target in IPF. Combined with the results of our study, therapeutic agents specifically targeting small airways have great potential in the early stage of IPF. Our study also provided clinically selectable indicators for monitoring the efficacy of those targeted drugs.

There were some limitations in our study. Firstly, this was a retrospective study subjected to inherent limitations associated with retrospective analyses. Moreover, our study was a cross-sectional study that lacked a longitudinal comparison of small airway functions of IPF patients. Another limitation was that survival data was absent in our study, which is critical for analyzing the association between SADs and survival outcomes. Therefore, as an initial study, there is still a long way to go to explore potential mechanisms, clinical characteristics, and therapeutic strategies targeting SAD.

Assessment of IPF lung physiology using PFT demonstrates that SAD was a common lesion of IPF patients and occurred at the early stages of the disease. Both spirometry and IOS exhibited diagnostic capabilities in the diagnosis of SAD. And IOS showed higher sensitivity than spirometry, especially for patients with abnormal lung function, revealing the necessity of combining two tools in diagnosing SAD. Our study suggested the necessity of monitoring the small airway function of IPF, which was also neglected in the clinic previously. We also provided efficient tools for SAD detection in IPF patients. It should be emphasized that SAD could happen at the early stage of the disease, and small airways could be a potential therapeutic target for IPF patients.

Full names	Abbreviations
*SAD*	small airway dysfunction
*IOS*	Impulse oscillometry system
*GAP*	Gender, age, and 2 lung physiology variables (FVC and DL CO)
*PSM*	propensity score matching
*FVC*	forced vital capacity
*FEV1*	forced expiratory volume in 1 second
RV	residual volume
*DLco*	the diffusing capacity for carbon monoxide
*MEF*	maximum expiratory flow
*MMEF*	maximal mid-expiratory flow
OR	odds ratio
CI	confidence interval
R5	resistances at 5 Hz
*R20*	resistances at 20 Hz
*R5-R20*	resistances at 5 and 20 Hz
X5	reactance at 5 Hz
*Fres*	resonant frequency in Hz

Ethics approval and consent to participate

All patients in this study were informed at admission that their medical records were likely to be used for clinical studies. This study was approved by the Institutional Review Board of Sixth People's Hospital (IRB approval number: 2017-090)

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

Conceived and designed the experiments: Tao Ren, Wenlan Yang, Yi Lu, Yuqiong Lei; Collected the data: Yuqiong Lei, Haotian Dai, Zeyu Liu, Yongle Xu, Qi Zheng; Analyzed the data: Yuqiong Lei; Contributed analysis tools: Cheng Zhong, Zeyu Liu; Create the diagrams: Yuqiong Lei, Jingyuan Zhang; Wrote the paper: Yuqiong Lei, Haotian Dai, Jingyuan Zhang; Revised the paper: Tan Ren, Wenlan Yang, Yi Lu. All authors read and approved the final manuscript.

Funding

This study was supported by the Three-year Action Plan of Shanghai Shenkang Hospital Development Center (SHDC2020CR3063B).

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Acknowledgements

We would like to express our gratitude to all colleagues for their assistance in screening eligible patients and gathering data.

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No competing interests reported.

Additionalfiles1.docx
Additional file 1 Concordance between spirometry and different IOS parameters. The criterias of SAD based on R5-R20, X5 and Fres exhibited enhanced sensitivity compared with spirometric criteria.
Additionalfiles2.docx
Additional file 2 GAP score of different GAP stage This table exhibitd GAP score distribution of different GAP stage.
Additionalfiles3.docx
Additional file 3 Differences in IOS parameters between GAP stage subgroups and control group. Compared IOS parameters of controls, GAP I patients and GAP II-III patients, we found R5-R20, X5, Fres deteriorated in GAP II-III patients.
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Small airway dysfunction is an early physiological characteristic of idiopathic pulmonary fibrosis : a retrospective cohort study

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